Home » Alternative Data » $FB Decline 19%- What did the Social and technical analysis show? – July 26 2018

$FB Sell Signals in Social Media and TA-LIB

Many were surprised by the earnings announcement on Facebook which resulted in a massive 19% drop in the value of the stock closing at $176.26. $120 Billion of market capitalization was lost. $FB founder and CEO Mark Zukerburg personally lost $15 Billion. Brokerage firms dropped their target price per shares all over Wall Street.

$FB Drops 19% on poor earnings

Whenever something like this happens it gets me thinking. What were the signals that could have helped you see the drop? Were there warning signs?

So after looking for a few minutes, I found a few interesting things that could use some research.

Social Market Analytics (SMA) was reporting (before the close) a bearish S-Score of -1.248. The S-Score is their proprietary measurement of the deviation of a stock’s sentiment intensity level from a normal state. CloudQuant is very much in favor of Alternative Data Sets like those provided by SMA.

Many crowd researchers using CloudQuant are proponents of Technical Analysis and the TA-LIB library for Python. The TA-LIB can produce many signals for Buy or Sell. Sometimes the signals disagree with each other. However, on July 24th there were only two TA-LIB patterns that provided a signal:

HighWaveCandle indicated sell

SpinningTop indicated sell

Both of these signals were a complete reverse from the previous day’s buy signals.

I haven’t had time to do further research – but would be interested in knowing if these few observed patterns can be used to become an alpha signal.

The table below shows all the TA-LIB signals for March 5 2018 to July 25 2018 based on Daily Market Data Bars.

The thoughts and opinions on this site do not represent investment recommendations by CloudQuant or Kershner Trading Group. Securities, charts, illustrations and other information contained herein are provided to assist crowd researchers in their efforts to develop algorithmic trading strategies for backtesting on CloudQuant.